Reagent calibration system and method

ABSTRACT

One aspect relates to a method of calibrating event data. The method includes obtaining, via an electronic device including a processor, event data for an assay including a reagent. The reagent is associated with one of a plurality of manufacturing lots of the reagent. The method includes receiving one or more calibration factors for the reagent based on an identifier associated with the one of the plurality of manufacturing lots. The method further includes generating calibrated event data based on an application of the one or more calibration factors to the event data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/300034 filed on Sep. 28, 2016 and entitled “REAGENT CALIBRATIONSYSTEM AND METHOD” which is a national phase of PCT Application No.PCT/US2015/014681, filed on Feb. 5, 2015, which claims the benefit ofU.S. Provisional Application No. 61/979,300, filed on Apr. 14, 2014,each of which are incorporated herein by reference in their entirety.Furthermore, any and all priority claims identified in the ApplicationData Sheet, or any correction thereto, are hereby incorporated byreference under 37 C.F.R. § 1.57.

FIELD

This disclosure relates to assay reagent calibration, and in particularto reagent based calibration of quantitative results as flow cytometermean fluorescence intensity results.

BACKGROUND

Particle analyzers, such as flow and scanning cytometers, are analyticaltools that enable the characterization of particles on the basis ofoptical parameters such as light scatter and fluorescence. In a flowcytometer, for example, particles, such as molecules, analyte-boundbeads, or individual cells, in a fluid suspension are passed by adetection region in which the particles are exposed to an excitationlight, typically from one or more lasers, and the light scattering andfluorescence properties of the particles are measured. Particles orcomponents thereof typically are labeled with fluorescent dyes tofacilitate detection. A multiplicity of different particles orcomponents may be simultaneously detected by using spectrally distinctfluorescent dyes to label the different particles or components. In someimplementations, a multiplicity of photodetectors, one for each of thescatter parameters to be measured, and one for each of the distinct dyesto be detected are included in the analyzer. The data obtained comprisethe signals measured for each of the light scatter parameters and thefluorescence emissions.

Cytometers may further comprise means for recording the measured dataand analyzing the data. For example, data storage and analysis may becarried out using a computer connected to the detection electronics. Forexample, the data can be stored in tabular form, where each rowcorresponds to data for one particle, and the columns correspond to eachof the measured parameters. The use of standard file formats, such as an“FCS” file format, for storing data from a flow cytometer facilitatesanalyzing data using separate programs and/or machines. Using currentanalysis methods, the data typically are displayed in 2-dimensional (2D)plots for ease of visualization, but other methods may be used tovisualize multidimensional data.

The parameters measured using a flow cytometer typically include theexcitation light that is scattered by the particle along a mostlyforward direction, referred to as forward scatter (FSC), the excitationlight that is scattered by the particle in a mostly sideways direction,referred to as side scatter (SSC), and the light emitted fromfluorescent molecules in one or more channels (range of frequencies) ofthe spectrum, referred to as FL1, FL2, etc., or by the fluorescent dyethat is primarily detected in that channel. Different cell types can beidentified by the scatter parameters and the fluorescence emissionsresulting from labeling various cell proteins with dye-labeledantibodies.

Both flow and scanning cytometers are commercially available from, forexample, BD Biosciences (San Jose, Calif.). Flow cytometry is describedin, for example, Landy et al. (eds.), Clinical Flow Cytometry, Annals ofthe New York Academy of Sciences Volume 677 (1993); Bauer et al. (eds.),Clinical Flow Cytometry: Principles and Applications, Williams & Wilkins(1993); Ormerod (ed.), Flow Cytometry: A Practical Approach, OxfordUniv. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols,Methods in Molecular Biology No. 91, Humana Press (1997); and PracticalShapiro, Flow Cytometry, 4th ed., Wiley-Liss (2003); all incorporatedherein by reference. Fluorescence imaging microscopy is described in,for example, Pawley (ed.), Handbook of Biological Confocal Microscopy,2nd Edition, Plenum Press (1989), incorporated herein by reference.

The data obtained from an analysis of cells (or other particles) bymulti-color flow cytometry are multidimensional, wherein each cellcorresponds to a point in a multidimensional space defined by theparameters measured. Populations of cells or particles are identified asclusters of points in the data space. The identification of clustersand, thereby, populations can be carried out manually by drawing a gatearound a population displayed in one or more 2-dimensional plots,referred to as “scatter plots” or “dot plots,” of the data.Alternatively, clusters can be identified, and gates that define thelimits of the populations, can be determined automatically. Examples ofmethods for automated gating have been described in, for example, U.S.Pat. Nos. 4,845,653; 5,627,040; 5,739,000; 5,795,727; 5,962,238;6,014,904; 6,944,338; and U.S. Pat. Pub. No. 2012/0245889, eachincorporated herein by reference.

It will be appreciated that due, in part, to the scale at which flowcytometers operate, even minor variations can have significant impactson the resulting data. Variations can be introduced based on the reagentused, the manufactured lot of the reagent used, conditions when thereagent is used (e.g., temperature, humidity, barometric pressure),instrument used to generate the data, and so on. These variables canlead to confusion in interpreting the results as a given populationcluster which is identified during a first test may appear in adifferent location, or not at all, in a subsequent test. As such, it isdesirable to provide a reliable way to generate reproducible event dataresults that may also be reliably compared with past or future data.

SUMMARY

The systems, methods, and devices of the disclosure each have severalinnovative aspects, no single one of which is solely responsible for thedesirable attributes disclosed herein.

In one innovative aspect, a method is provided. The method includesobtaining, via an electronic device including a processor, event datafor an assay including a reagent. The reagent is associated with one ofa plurality of manufacturing lots of the reagent. The method includesreceiving one or more calibration factors for the reagent based on anidentifier associated with the one of the plurality of manufacturinglots. The method further includes generating calibrated event data basedon an application of the one or more calibration factors to the eventdata.

In another innovative aspect, a calibration device is provided. Thedevice includes an event data receiver configured to receive event datafor an assay including a reagent associated with one of a plurality ofmanufacturing lots. The device also includes a calibrator configured toobtain one or more calibration factors for the reagent based on anidentifier associated with the one of the plurality of manufacturinglots. The device further includes an event data processor configured togenerating calibrated event data based on an application of the one ormore calibration factors to the event data.

In a further innovative aspect, a non-transitory computer readablemedium comprising instructions executable by a processor of an apparatusis provided to perform one or more of the innovative methods describedherein.

In yet another innovative aspect, an apparatus comprising a processor,configured to perform any of the methods of described herein isprovided.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a functional block diagram of a system for reagentcalibration.

FIG. 2 illustrates a functional block diagram of an example of a reagentcalibration system.

FIG. 3 illustrates a call flow diagram of an example message exchangefor generating calibration information for a lot of a reagent.

FIG. 4 illustrates a call flow diagram of an example message exchangefor calibrating cytometric data obtained using a lot of a reagent.

FIG. 5 shows a functional block diagram of an example reagentcalibrator.

FIG. 6 shows a process flow diagram of a method of reagent calibrationincluding aspect described above.

FIG. 7 shows a process flow diagram for a method of reagent calibration.

DETAILED DESCRIPTION

The features described are generally applicable to the field of flowcytometry, such as in applications that require the quantitativeassessment of the median fluorescence intensity (MFI), whichconsequently requires reagents with tight MFI tolerances. The latterrequires manufacturing the reagents under tight MFI control, which is anextremely difficult, lengthy, and costly process that does not evenguarantee the minimization of MFI variability due to reagents lot-to-lotmanufacturing variability. While reference is made specifically to MFIof a reagent, other variable reagent characteristics such a lightscatter (e.g., forward scatter (FSC); side scatter (SSC)), the peakfluorescence wavelength of a reagent, and the fluorescence decay timemay be calibrated via the aspects described.

Described in further detail below are methods and systems for creating aset of correction factors for a set of flow cytometry reagents andsubsequently using these factors to numerically adjust the data measuredfrom a test sample to reduce variability that may be caused bylot-to-lot differences in the manufacturing of the reagents.

The calibration factors may be used to scale and/or correct event dataof the cytometry data. The factors may be applied before or afterperforming spectral overlap compensation for MFI event data. Thecalibration factors may be also applied as a factor in calculating theSOVs (spillover values) used to determine the compensated values.Furthermore, the calibration file may also include the lot manufacturingdate and other values, which can be used to compensate for the naturaldecay in the reagent with time post manufacturing.

One non-limiting advantage of the features described is facilitation ofthe reagent manufacturing process by relaxing the reagent specificationsand manufacturing restriction, such as the MFI (median fluorescenceintensity) specifications restrictions. Practically, this allows anyreagent lot, with appropriate calibration, to meet the accuracyspecifications required for population discrimination in themultidimensional classification space.

The new invention will help resolve some of the difficulties encounteredin reagent manufacturing when tight response (e.g., MFI) control isdesired. An example of these difficulties is that existing conjugates inpanels are not designed for tight MFI control. For these panels, thechemistry processes are not validated for MFI control. Instead, thechemistry is validated to control yield. Another example of thesedifficulties is the scope of control needed to support multidimensionalchemistries. In some implementations, the reagent may providen-dimensional data to provide multiple layers of detection. Thisincreases the factors which must simultaneously be controlled for agiven reagent. An entire lot may be rendered useless if a manufacturingdefect is identified in just one dimension.

Customers may also reap several non-limiting benefits from the describedaspects. For example, once purchased, a reagent may be accuratelycalibrated over time. This can enhance the shelf-life of a given reagentthereby reducing waste and repurchasing needs. Furthermore, thestandardization of the results can allow a body of knowledge to developfor a given assay. This allows meaningful comparisons of data acrosssamples and over time. These comparisons, in turn, can lead to, forexample, faster analysis of test results.

The described features obviates the need for complicated manufacturingprocesses needed to deliver reagents with tight specifications (e.g.,fluorescence intensity) that are used to meet the accuracy requirementsof multidimensional cytometry classification methods. Currently, thereare very few single color products in flow cytometry that guarantee aconsistent median fluorescence intensity (MFI) output. Flow cytometryreagents are meant as qualitative reagents optimized for the separationof negative from positive populations, not as a quantitative tool.

However, in recent years, flow cytometry reagents have been used withmore quantitative purposes. These quantitative needs often rely on MFIas an output. As noted above, flow cytometry data, such as MFI eventdata, is very difficult to control due to the large number of variablesinvolved in the manufacturing, conjugation and handling of fluorescentantibodies as well as the large variability that working with biologicalsubstances, like proteins and dyes brings.

Asking manufacturing to produce a product with consistent MFI from lotto lot is an immensely difficult task that would require strict controlsat every step of the production process, making this extremely expensiveand not cost effective. When thinking about a multicolor flow cytometryproduct, this problem is only made worse by spillover and interactionsbetween different fluorochromes and conjugates, making MFI consistencyalmost impossible.

Instead of forcing the reagent (which has biological and chemicallimitations) to fit within an desired specification, systems and methodsto create a numerical correction factor that allows any lot of reagentmanufactured that passes existing quality control specifications tostill fit the desired specification are described. Since all functionaltesting performed in quality control uses the linear range of theinstrument, this correction factor could be applied and generate acorrect approximate result from any lot of reagent produced bymanufacturing no matter how distant it is from the reference targetreading. This numeric factor can be generated as a deviation from areference value, and applied mathematically through a module implementedvia software, hardware, or a hybrid of these to each particular lot ofreagent made. This applies not only to single color reagents, but alsoto multicolor products. This correction factor could not only be appliedto correct MFI variations resulting from lot to lot differences inmanufacturing, but could also help with spillover correction and evenaccount for activity decay of the reagent over time.

As used herein, “system” and “instrument” and “apparatus” generallyencompass both the hardware (e.g., mechanical and electronic) andassociated software (e.g., computer programs) components.

As used herein, an “event” generally refers to the data measured from asingle particle, such as cells or synthetic particles). Typically, thedata measured from a single particle are include a number of parameters,including one or more light scattering parameters, and at least onefluorescence intensity parameters. Thus, each event is represented as avector of parameter measurements, wherein each measured parametercorresponds to one dimension of the data space.

FIG. 1 shows a functional block diagram of a system for reagentcalibration. The system 100 includes a cytometer 102. The cytometer 102may be a flow and/or scanning cytometer such as those commerciallyavailable from, for example, BD Biosciences (San Jose, Calif.). Thecytometer 102 may be configured to receive a biological sample alongwith one or more reagents. Using the received sample and reagent(s), thecytometer 102 may generate event data. The event data may include one ormore events which represent a reading for a particular portion (e.g.,cell) of the sample. One example reading is a median fluorescenceintensity (MFI). The event data may include an identifier for thereagent used to generate the event data. The event data may include alot identifier for the reagent indicating which manufacturing lot of thereagent was used to generate the event data. The event data may furtherinclude one or more contextual information elements such as the time theevent data was generated, date on which the event data was generated,geospatial location where the event data was generated, instrument usedto generate the data, instrument configuration information for thecytometer used to generate the event data, or operator who performed theexperiment.

The cytometer 102 may provide the event data to a cytometric dataanalysis system 104. The cytometric data analysis system 104 isconfigured to process and analyze the event data. For example, analysismay include identifying a “population” or “subpopulation” of particles,such as cells or other particles, representing a group of particles thatpossess optical properties with respect to one or more measuredparameters such that measured parameter data form a cluster in the dataspace. Populations may be recognized as clusters in the data. A clustermay be defined in a subset of the dimensions, e.g., with respect to asubset of the measured parameters, which corresponds to populations thatdiffer in only a subset of the measured parameters. The pattern ofclusters (e.g., number, location, size) can be used to identifypathological outcomes, such as a type of cancer. The cytometric dataanalysis system 104 may be configured to “gate” the event data. Gatinggenerally refers to defining a set of boundary points identifying asubset of data of interest. In cytometry, a gate may bound a group ofevents of particular interest. As used herein, “gating” generally refersto the process of defining a gate for a given set of data. Thecytometric data analysis system 104 may be configured to pre or postprocess event data such as formatting, unit conversion, standardization,encryption, or the like.

The cytometric data analysis system 104 may be implemented as dataanalysis software or data acquisition software executable by a processoron a computing device. The cytometric data analysis system 104 may beimplemented as a stand-alone data scaling application. In suchimplementations, the cytometric data analysis system 104 may provide asan executable module or as a networked based service. The cytometricdata analysis system 104 may be implemented in firmware and configuredto perform one or more of the features described herein. In someimplementations, the cytometric data analysis system 104 may beintegrated with the cytometer 102.

The cytometer 102 and the cytometric data analysis system 104 shown inFIG. 1 each include a reagent calibrator 500A and 500B (collectively andindividually referred to hereafter as reagent calibrator 500). Thereagent calibrator 500 is configured to obtain one or more event datacalibration factors to adjust collected event data. The reagentcalibrator 500 is described in further detail below, such as inreference to FIG. 5.

The cytometer 102 may be configured to communicate directly with thecytometric data analysis system 104. In some systems, the cytometer 102and the cytometric data analysis system 104 may communicate with eachother and other systems via a network 160.

Examples of the network 160 include a wide area network (WAN),metropolitan area network (MAN), local area network (LAN), wirelesslocal area network (WLAN), or personal area network (PAN). Althoughshown as one network, the network 160 may include several interconnectednetworks. The networks which may be included in the system 100 maydiffer according to the switching and/or routing technique used tointerconnect the various network nodes and devices (e.g., circuitswitching vs. packet switching), the type of physical media employed fortransmission (e.g., wired vs. wireless), and the set of communicationprotocols used (e.g., Internet protocol suite, SONET (SynchronousOptical Networking), Ethernet, etc.). Regardless of the form the network160 may take, the network 160 is configured to facilitatemachine-to-machine messaging for reagent calibration as described infurther detail herein.

The system 100 of FIG. 1 includes a reagent calibration system 200 asone example of another system the cytometer 102 and the cytometric dataanalysis system 104 may exchange communications. In someimplementations, the reagent calibrator 500 is configured to process thecommunications for the associated cytometer or cytometric data analysissystem. The reagent calibration system 200 is configured to generate andprovide reagent calibration information as described in further detailbelow. The calibration information may be stored in a calibrationstorage 114 included in the system 100. In some implementations, thecalibration storage 114 may be accessed via the network 160. In suchimplementations, the calibration storage 114 may be implemented as acloud storage device. The calibration storage 114 may include a databaseor other information storage system to facilitate efficient storing andretrieving of calibration information.

FIG. 2 illustrates a functional block diagram of an example of a reagentcalibration system. The reagent calibration system 200 may be configuredto receive a manufactured lot of a reagent and generate one or morecalibration factors for the lot. The reagent calibration system 200 maybe configured to receive a request for calibration factors associatedwith a specified reagent lot.

To generate the calibration factors, the reagent calibration system 200includes a lot data receiver 202. The lot data receiver 202 isconfigured to receive event data for a particular manufactured lot of areagent. The event data may be received via a communication input/output206.

The communication input/output 206 may be configured to transmit andreceive messages through wired or wireless communication channels. Inone implementation, the communication input/output 206 may comprise anetwork card. The communication input/output 206 may provide thereceived information to an element included in the reagent calibrationsystem 200. In some implementations, the communication input/output 206may store the received information in a memory 208.

The memory 208 may include both read-only memory (ROM) and random accessmemory (RAM). The memory 208 may provide instructions and data to theprocessor 212. A portion of the memory 208 may also include non-volatilerandom access memory (NVRAM).

The reagent calibration system 200 may include a processor 212 whichcontrols operation of the reagent calibration system 200. The processor212 may also be referred to as a central processing unit (CPU). Theprocessor 212 may perform logical and arithmetic operations based onprogram instructions stored within the memory 208. The instructions inthe memory 208 may be executable to implement aspects of the methodsdescribed herein. The elements included in the reagent calibrationsystem 200 may be coupled by a bus 216. The bus 216 may be a data bus,communication bus, or other bus mechanism to enable the variouscomponents of the system 200 to exchange information. It will further beappreciated that while different elements have been shown, multiplefeatures may be combined into a single element, such as a calibrationfactor generator and calibration engine into a single calibrationcomponent.

The lot data receiver 202 may be configured to determine whethercalibration factors have already been developed for the specifiedreagent and/or lot. If no factors have been generated, the lot datareceiver 202 may determine that the lot data will be the reference lot.The determination may be based on information included in the lot datareceived. For example, the lot data may include a value indicating thelot data is a reference lot. The value may be provided, in oneimplementation, via a header field. The determination may be based onadditional input information. For example, the lot data receiver 202 mayprovide a message for presentation to an operator indicating nocalibration exists for the specified reagent and requesting eitherre-identification of the reagent or confirmation to use the lot data asa reference lot.

A calibration factor generator 210 may obtain the lot data and generateone or more calibration factors for the reagent lot. If the lot is areference lot, the calibration factors may include assigning acalibration formula for the reagent. For example, the reagent mayprovide four dimensions of spectral data. Accordingly, the reagent maybe associated with an equation including four terms, one for eachdimension. The equation may be provided via an input device or retrievedfrom a library of stored terms and equations. The equation may includecontextual adjustments such as: time from date of manufacture,environment (e.g., temperature, humidity, barometric pressure),instrument type, instrument configuration, and instrument state (e.g.,temperature, available memory, available processor power, schedule),instrument operator.

If the lot is not a reference lot, the calibration factor generator 210is configured to retrieve the calibration information, if any, and eventdata for the reference lot of the identified reagent. By comparing theevent data from the reference lot to the measured event data from themanufactured lot under test, the specific factors needed to align themeasured event data with the reference event data may be generated. Thefactors may be expressed, in one implementation, as a deviation from areference value. The calibration factor generator 210 may be configuredto include context information as part of the generation of thecalibration factors for a given lot.

The factors generated by the calibration factor generator 210 may bestored in the memory 208. In some implementations, the factors may bestored in the calibration storage 114 shown in FIG. 1. The calibrationfactor generator 210 may be configured to store the calibrationinformation in association with one or more identifiers for the reagentand manufacture lot used to generate the calibration information.

FIG. 3 illustrates a call flow diagram of an example message exchangefor generating calibration information for a lot of a reagent. FIG. 3shows messages exchanged between several entities which may beconfigured for reagent calibration. It will be understood that otherentities may be included as intermediaries but have been omitted fromFIG. 3 for clarity purposes only.

At message 302, the cytometer 102 receives a known sample and a reagentfrom a manufacturing lot. The cytometer 102, at message 304, generatesthe event data for the lot. The event data may include, for example, MFIevent information. The cytometer, at message 306, transmits the lot dataand reagent identification to the lot data receiver 202. In someimplementations, the cytometer 102 may be configured to transmit contextinformation as described above for the lot. The lot data receiver 202,at message 308, verifies the reagent information. Verification mayinclude determining whether the identified reagent is a valid reagent(e.g., a reagent for which the system can provide calibration). Thisverification may include processing the reagent identificationinformation such as via a hashing algorithm, to determine the validityof the reagent's identity. Verification may include determining whethera reference lot has already been received for this reagent. If so, thelot data receiver 202 may terminate the process with a messageindicating the presence of a previous reference lot or continueevaluation of the lot data as a manufacture lot for which calibrationfactors will be generated to align the manufactured lot with apreviously processed reference lot.

The lot data receiver 202, at message 310, may be configured to verifythe lot data. Lot data verification may include verifying the authorityof the information to serve as a lot for calibration. For example, anauthorization token may be included in the lot data indicating the datais trusted for the purpose of defining calibration information. Theauthority may be based on an identifier for the cytometer generating thelot data. For example, it may be desirable to only accept reference datalots from specifically configured and/or located cytometers.Verification may include ensuring the operator of the cytometer 106 isauthorized to submit lot data. Lot data verification may includedetermining the sufficiency of the lot data to serve as a reference lotor sufficient to calibrate. For example, it may be desirable to requirea minimum number of events in a given set of lot data to serve as areference lot or lot which can reliably be used to generate calibrationfactors. If the lot data fails verification, the lot data receiver mayterminate the process with a message indicating the deficiency with theprovided lot data.

Having verified the reagent and the data, at message 312, the lot datareceiver 202 provides the lot data and reagent information to thecalibration factor generator 210. The calibration factor generator 210is configured to generate, via message 314, the calibration record forthe reference lot. Generating the record may include generating a uniqueidentifier for the reagent lot. The process terminates, in someimplementations, with a message indicating successful creation of thecalibration record for the lot. In some implementations, this messagemay also include the identifier for the calibration record.

The calibration factors or the identifiers to obtain the factors may beprovided along with the reagent to customers. For example, the reagentmay be shipped with a non-volatile computer readable media (e.g.,CD-ROM, DVD-ROM, USB memory stick, floppy disk, Secure Digital card)which include the calibration information for the reagent lot. In someimplementations, a customer may use the identifier to obtain thecalibration information. For example, a code may be included on thereagent which can be transmitted to the reagent calibration system 200.Via the communication input/output 206, the reagent calibration system200 receives the code and a calibration engine 204 determines whichcalibration factors to provide. The calibration engine 204 may determinethe calibration factors to provide by decoding the input codeinformation to determine the reagent and lot of interest. Using thereagent and lot information, the calibration engine 204 may perform alookup for the calibration information. In some implementations,calibration engine 204 may be configured to adjust the calibrationinformation. For example, because time may be a calibration factor, oneor more elements included in the calibration information may needfurther refinement due to a different between when the factor wasgenerated and the time at which the factor is requested. The calibrationinformation may be transmitted to the requesting device via thecommunication input/output 206.

FIG. 4 illustrates a call flow diagram of an example message exchangefor calibrating cytometric data obtained using a lot of a reagent. FIG.4 shows messages exchanged between several entities which may beconfigured for reagent calibration. It will be understood that otherentities may be included as intermediaries but have been omitted fromFIG. 4 for clarity purposes only.

At message 402, a test sample treated with an identified lot of areagent is provided to the cytometer 106. The cytometer 106 may be thesame cytometer used to generate the calibration information or adifferent cytometer, such as one located at a reagent customer'slaboratory.

The cytometer 106, via message 404, executes the specified test togenerate event data. The event data may include median fluorescentintensity information for events.

The raw event data may be provided, via message 406, to the reagentcalibrator 500. The reagent calibrator 500 may also receive informationidentifying the reagent and lot used to generate the raw event data.

Message 408 may be transmitted from the reagent calibrator 500 to thecalibration engine 204. The message 408 may include the informationidentifying the reagent and lot. In some implementations this may be theidentifier provided with the reagent, as discussed above.

The calibration engine 204, at message 410, verifies the reagentinformation. Verification of the reagent information may includedetermining whether calibration information exists for the specifiedreagent and/or lot. In some implementations, it may be desirable toprovide the calibration information on a subscription basis toauthorized users. In such implementations, verification may includeidentifying an authorization value and determining whether the value ispermitted to receive the requested calibration information, in whole orin part. If the verification fails, a message (not shown) may beprovided including a value indicating the error.

If verified, at message 412, calibration information for the requestedreagent lot is generated. Generating the calibration information mayinclude looking up the calibration information from the calibrationstorage 114. In some implementations, further refinement of thecalibration information in the calibration storage 114 may be performedby the calibration engine 204 to account for dynamic information such astime elapsed since manufacture of the lot. The calibration informationmay be stored in a cache of the calibration engine 204. In suchimplementations, before performing lookups or computations to producethe calibration information, the calibration engine 204 may consult acache memory for the requested information. Conventional cachingalgorithms may be implemented to manage the cache memory space such asaccess frequency based caching or random replacement caching.

Once generated, the calibration information is sent to the reagentcalibrator 500 via message 414. The reagent calibrator 500 may thencalibrate the raw event data to generate calibrated event data atmessage 416. The calibrated event data may be transmitted to anotherentity or stored for further processing. In some implementations, theraw event data may be stored along with the calibrated event data. Insome implementations, the raw event data may be stored along with thecalibration information. In some implementations, the calibrated eventdata along with the calibration information may be stored.

FIG. 5 shows a functional block diagram of an example reagentcalibrator. The reagent calibrator 500 includes an event data receiver502. The event data receiver 502 is configured to receive the raw eventdata. When integrated in a flow cytometer, such as the reagentcalibrator 500A, the raw event data may be provided from a sampleanalysis module of the cytometer. In some implementations, the eventdata receiver 502 may receive the raw event data from memory or asstreaming data. The event data receiver 502 is further configured toidentify the lot and reagent information from the received event data.The identification may include parsing one or more header fields of theevent data to obtain an identifier for the reagent and/or lot.

The identification information for the reagent and lot are provided to acalibration factor retriever 504. The calibration factor retriever 504is configured to retrieve calibration information for a given reagentlot. The retrieval process includes determining a source for thecalibration information and initiating the communications with thesource to retrieve the calibration information. For example, somecalibration information may be provided via a non-volatile memory. Insuch implementations, the calibration factor retriever 504 may beconfigured to determine whether the volume including the calibrationinformation has been mounted. A factor request may perform a look up onthe volume or within a file included on the volume. In someimplementations, the calibration information may be provided via networkcommunications with a calibration engine. In such implementations, thecalibration factor retriever 504 may be configured to generate andtransmit a factor request to the calibration engine and receive theinformation sent in response. The factor request may include theinformation identifying the reagent and lot.

To facilitate the various retrieval mechanisms that may be used forvarious reagents, the calibration factor retriever 504 may receive aretrieval configuration. The retrieval configuration may identify thetype of source and provide the information needed to communicate withthe source. For example, if a volume should be mounted, a volume namemay be specified in the configuration. As another example, if acalibration engine is used for obtaining the calibration information,endpoint information (e.g., URL, IP address) and security information(e.g., username, password, authorization token) may be included in theconfiguration.

The calibration factor retriever 504 provides the calibrationinformation to an event data calibrator 506. The event data calibrator506 also receives the events included in the event data. The event datacalibrator then applies the calibration information to each event togenerate calibrated event data.

FIG. 6 shows a process flow diagram of a method of reagent calibrationincluding aspect described above. The method may be implemented in wholeor in part by the reagent calibrator 500 and the reagent calibrationsystem 200.

At node 602, a reference lot and one or more additional lots of areagent are manufactured. At node 604, the reagents are each tested on aknown platform. The known platform includes a commonly configuredcytometer and a known sample. The test measures event information suchas median fluorescence intensity. At node 606, the reference lot eventinformation is stored. At node 608, the measured event data for anon-reference lot is compared with the measured event data for thereference lot to generate a calibration factor. The calibration factoraligns the measured event data with the reference event data. In someimplementations, the comparison may be the difference or ratio betweenthe reference and the actual values obtain for testing the non-referencelot. Other information that could be included in this correction factorsuch as, for example, spill over into other channels and reagentstability decay expected over time. This correction factor is associatedwith the particular lot number (e.g., via a reagent lot file, acalibration engine).

In some implementations, it may be desirable to use a known sample froma pool of multiple donors such as a multi-donor blood pool. Using amulti-donor sample pool for reagent calibration can generate calibrationfactors with increased reliability because the factors would moreclosely represent differences in the MFI variability. Having a varietyof biological sample sources can minimize the impact of the sampleitself (e.g., inherent biological differences of person A's blood ascompared to person B's blood) on the calibration factors. In one exampleimplementation, the calibration factors can be obtained by testing botha new-lot reagent(s) and a previous-lot reagent on the same pool ofmulti-donor blood and then generating the calibration factors bycomparing the MFI of the new-lot reagent to that of the previous-lotreagent.

At node 610, the lot of the reagent is provided along with thecalibration information or identification information to obtain thecalibration information. At node 612, a test is performed with theprovided lot of reagent to obtain raw event data. At node 614, thecalibration information is applied to the raw event data to generatecalibrated event data.

One non-limiting advantage of the described calibration features is theability to generate, such as via node 670, disease classifications basedon the reference event data. Because event data may be consistentlyproduced (e.g., calibrated), a classification library may be generatedthat can be used by researchers or other cytometric data analysissystems to analyze test data. Because the variation due to reagent lotcan be minimized or eliminated using the calibration information,meaningful comparisons can be made between event data for differenttests over time.

In such implementations, at node 680, the calibrated event data may beclassified based on the library of classifications generated at node670.

In an example embodiment the reagent(s) lot is tested against anestablished reference lot to determine the required calibration factors.This is followed by the generation of a calibration file including alookup table of the calibration factors and the date of lotmanufacturing. Cytometry data is acquired using the reagent(s) lot. Thecalibration file is then imported into the data analysis software andthe cytometry data resulting from each reagent (e.g., MFI) is adjustedby the corresponding calibration values. Once calibrated, populationclassification may be performed to provide an accurate and fast analysisof the data.

When a customer receives a lot of the reagent, the correction factorcould be applied manually by the user or in one embodiment, it couldautomatically be calculated and applied by the software based on the lotnumber and reagent lot file provided and the date the reagent is beingused and acquired on the flow cytometer. The correction (calibration)factor(s) would then ensure that no matter how different the actualmeasured characteristic (e.g., intensity) caused by the reagent itselfis coming out of manufacturing, the reagent measurement for a given lotwill be normalized and its performance, as far as the characteristic isconcerns, will be automatically matched to that of the expectedreference against which it was calibrated.

The features described provide aspects which account for reagentcharacteristics which are highly variable and difficult to control dueto the large number of factors that affect it, such as MFI. Bygenerating and applying a correction factor calculated from a referenceinstead of having tight manufacturing specifications, any existingreagent that passes existing QC specifications could be used forcharacteristic controlled products, such as MFI controlled products,including multicolor panels. This reduces the burden on manufacturingand relaxes QC specifications because the calibrated characteristics arediscrete measurable values that can be easily manipulated mathematicallywhen acquired under linear range of the flow cytometer. It will beappreciated that the aspects described are not limited to linearrelationships and may be applied for other calibration factors and/orequations. Since functional testing performed in QC typically uses thelinear range of the instrument, the calibration information could beapplied and generate a correct approximate result from any lot ofreagent produced by manufacturing no matter how distant it is from thereference target.

FIG. 7 shows a process flow diagram for a method of reagent calibration.The method may be implemented in whole or in part by the devicesdescribed herein such as the reagent calibrator 500.

At node 702, event data for an assay including one lot of a reagent froma plurality of lots of the reagent is obtained. Obtaining the event datamay be receiving the event data at a flow cytometer or receiving storedevent data at a flow cytometer or cytometric data analysis system. Theevent data may include multi-dimensional cytometric measurements such asmedian florescent intensity or spill over. At node 704, one or morecalibration factors for the reagent is received. The calibration factorsare received based on an identifier associated with the one lot of thereagent. Receiving the calibration factors may include receiving thecalibration factors from a memory or via network messaging with, forexample, a calibration engine. At node 706, calibrated event data isgenerated based on an application of the one or more calibration factorsto the event data.

As used herein, the terms “determine” or “determining” encompass a widevariety of actions. For example, “determining” may include calculating,computing, processing, deriving, investigating, looking up (e.g.,looking up in a table, a database or another data structure),ascertaining and the like. Also, “determining” may include receiving(e.g., receiving information), accessing (e.g., accessing data in amemory) and the like. Also, “determining” may include resolving,selecting, choosing, establishing, and the like.

As used herein, the terms “provide” or “providing” encompass a widevariety of actions. For example, “providing” may include storing a valuein a location for subsequent retrieval, transmitting a value directly tothe recipient, transmitting or storing a reference to a value, and thelike. “Providing” may also include encoding, decoding, encrypting,decrypting, validating, verifying, and the like.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: a, b, or c” is intended to cover: a, b, c,a-b, a-c, b-c, and a-b-c.

Those of skill in the art would understand that information and signalsmay be represented using any of a variety of different technologies andtechniques. For example, data, instructions, commands, information,signals, bits, symbols, and chips that may be referenced throughout theabove description may be represented by voltages, currents,electromagnetic waves, magnetic fields or particles, optical fields orparticles, or any combination thereof.

Those of skill in the art would further appreciate that the variousillustrative logical blocks, modules, circuits, and algorithm stepsdescribed in connection with the embodiments disclosed herein may beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, circuits,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. Skilled artisans may implement thedescribed functionality in varying ways for each particular application,but such implementation decisions should not be interpreted as causing adeparture from the scope of the present invention.

The techniques described herein may be implemented in hardware,software, firmware, or any combination thereof. Such techniques may beimplemented in any of a variety of devices such as general purposescomputers, wireless communication devices, or integrated circuit deviceshaving multiple uses including application in wireless communicationdevice handsets and other devices. Any features described as modules orcomponents may be implemented together in an integrated logic device orseparately as discrete but interoperable logic devices. If implementedin software, the techniques may be realized at least in part by acomputer-readable data storage medium comprising program code includinginstructions that, when executed, performs one or more of the methodsdescribed above. The computer-readable data storage medium may form partof a computer program product, which may include packaging materials.The computer-readable medium may comprise memory or data storage media,such as random access memory (RAM) such as synchronous dynamic randomaccess memory (SDRAM), read-only memory (ROM), non-volatile randomaccess memory (NVRAM), electrically erasable programmable read-onlymemory (EEPROM), FLASH memory, magnetic or optical data storage media,and the like. The computer-readable medium may be a non-transitorystorage medium. The techniques additionally, or alternatively, may berealized at least in part by a computer-readable communication mediumthat carries or communicates program code in the form of instructions ordata structures and that can be accessed, read, and/or executed by acomputer, such as propagated signals or waves.

The program code may be executed by a processor, which may include oneor more processors, such as one or more digital signal processors(DSPs), general purpose microprocessors, an application specificintegrated circuits (ASICs), field programmable logic arrays (FPGAs), orother equivalent integrated or discrete logic circuitry. Such aprocessor may be configured to perform any of the techniques describedin this disclosure. A general purpose processor may be a microprocessor;but in the alternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Accordingly, the term “processor,” as used herein mayrefer to any of the foregoing structure, any combination of theforegoing structure, or any other structure or apparatus suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated software modules or hardware modules configured for encodingand decoding, or incorporated in a combined video encoder-decoder(CODEC).

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

Various embodiments of the invention have been described. These andother embodiments are within the scope of the following claims.

1.-10. (canceled)
 11. A method, comprising: obtaining, via an electronicdevice including a processor, event data for an assay, said assayassociated with one of a plurality of manufacturing lots; detecting, viathe electronic device, an identifier affixed on packaging material forsaid assay, wherein the identifier is associated with the one of theplurality of manufacturing lots; transmitting, via a network, a requestfor one or more calibration factors for said assay, wherein thererequest includes the identifier; receiving, via the network, the one ormore calibration factors for the one of the plurality of manufacturinglots of the assay; and generating calibrated event data based on anapplication of the one or more calibration factors to the event data.12. The method of claim 11, further comprising: calibrating a flowcytometer for the assay, wherein the flow cytometer is calibrated usingat least one of the one or more calibration factors; and analyzing, viathe electronic device, a sample to generate the event data.
 13. Themethod of claim 11, wherein receiving said one or more calibrationfactors includes: receiving a calibration factor for respectivedimensions of spectral data detected using a reagent included in saidassay.
 14. The method of claim 11, further comprising: storing said oneor more calibration factors in a machine-readable medium; and retrievingsaid one or more calibration factors from the machine-readable medium.15. The method of claim 11, wherein said one or more calibration factorsinclude a mean fluorescence intensity calibration factor.
 16. The methodof claim 11, wherein the event data comprises flow cytometry event data.17. The method of claim 16, wherein the flow cytometry event dataincludes median florescence intensity measurements.
 18. The method ofclaim 11, wherein the request includes an instrument identifier for aninstrument collecting the event data, and wherein the one or morecalibration factors include a contextual adjustment for the instrumentassociated with the instrument identifier.
 19. The method of claim 18,wherein the contextual adjustment is based on at least one of: atemperature of the instrument, memory available to the instrument,processor power available to the instrument, or an assay schedule of theinstrument.
 20. The method of claim 11, further comprising retrieving acalibration authorization value from a memory, wherein the request forthe one or more calibration factors includes the calibrationauthorization value.
 21. A system, comprising: an event data receiverconfigured to receive event data for an assay, said assay associatedwith one of a plurality of manufacturing lots; a calibrator configuredto: receive an identifier affixed on packaging material for said assay,wherein the identifier is associated with the one of the plurality ofmanufacturing lots; transmit, via a network, a request for one or morecalibration factors for said assay, wherein there request includes theidentifier; receive, via the network, the one or more calibrationfactors for the one of the plurality of manufacturing lots of the assay;and an event data processor configured to generate calibrated event databased on an application of the one or more calibration factors to theevent data.
 22. The system of claim 21, wherein the calibrator isfurther configured to calibrate a flow cytometer for the assay, whereinthe flow cytometer is calibrated using at least one of the one or morecalibration factors.
 23. The system of claim 21, wherein the one or morecalibration factors includes a calibration factor for respectivedimensions of spectral data detected using a reagent included in saidassay.
 24. The system of claim 21, wherein said one or more calibrationfactors include a mean fluorescence intensity calibration factor. 25.The system of claim 21, wherein the event data comprises flow cytometryevent data.
 26. The system of claim 25, wherein the flow cytometry eventdata includes median florescence intensity measurements.
 27. The systemof claim 21, wherein the request includes an instrument identifier foran instrument collecting the event data, and wherein the one or morecalibration factors include a contextual adjustment for the instrumentassociated with the instrument identifier.
 28. The system of claim 27,wherein the contextual adjustment is based on at least one of: atemperature of the instrument, memory available to the instrument,processor power available to the instrument, or an assay schedule of theinstrument.
 29. The system of claim 21, wherein the packaging materialincludes a vessel containing a reagent used in said assay.
 30. Thesystem of claim 21, wherein the event data processor is furtherconfigured to associate the event data with the identifier associatedwith the one of the plurality of manufacturing lots.